Comparative analysis of monocropping and mixed cropping systems on selected soil properties, soil organic carbon stocks, and simulated maize yields in drought-hotspot regions of Rwanda

Hashakimana, Léonidas; Tessema, Toru; Niyitanga, Fidèle; Cyamweshi, Athanase Rusanganwa; Mukuralinda, Athanase

doi:10.1016/j.heliyon.2023.e19041

Cited by 4 publications

(2 citation statements)

References 50 publications

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“…Samples obtained through Stratified Random Sampling are representative of the respective target population when there is maximum possible homogeneity of sampling units within each stratum and maximum possible heterogeneity among the strata, with respect to the characteristics or variables under consideration. Stratified Random Sampling is used in agricultural experiments mainly for measuring soil properties [24,25].…”

Section: Sampling Designs Under Considerationmentioning

confidence: 99%

Comparing Spatial Sampling Designs for Estimating Effectively Maize Crop Traits in Experimental Plots

Koutsos,

Menexes

2024

Agronomy

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The current study investigates the performance of various sampling designs in providing accurate estimates for crucial maize yield traits (intended for silage) including plant height, fresh/dry/ear weight, number of maize ears per plant, and total ear weight per plant, using spatial maize data. The experiment took place in an experimental field area at Aristotle University (AUTH) farm during the 2016 growing season. Nine sampling designs were statistically analyzed and compared with spatial data from an Italian maize hybrid (AGN720) to identify the most suitable and effective sampling design for dependable maize yield estimates. The study’s results indicate that, among the different sampling techniques, Stratified Random Sampling is the most effective and reliable method for obtaining accurate maize yield estimates. This new approach not only provides precise estimates but also requires fewer measurements, making it suitable for experiments where not all plants have emerged. These findings suggest that Stratified Random Sampling can be employed effectively as an alternative to harvesting the entire plot for effectively estimating maize crop traits in experimental plots.

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Section: Sampling Designs Under Considerationmentioning

confidence: 99%

Comparing Spatial Sampling Designs for Estimating Effectively Maize Crop Traits in Experimental Plots

Koutsos,

Menexes

2024

Agronomy

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“…In recent years, the accuracy of farmland SOC digital mapping has been remarkably improved by combining farmland planting and management systems, including cropping system, crop type, multiple cropping index, stubble index, and distance to irrigation canals [5,[11][12][13][14][15]. However, existing studies only address typical cropping systems in certain regions, where the systems are relatively straightforward [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Improving soil organic carbon mapping in farmlands using machine learning models and complex cropping system information

Ou,

Wu,

Yan

et al. 2024

Environ Sci Eur

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Obtaining accurate spatial maps of soil organic carbon (SOC) in farmlands is crucial for assessing soil quality and achieving precision agriculture. The cropping system is an important factor that affects the soil carbon cycle in farmlands, and different agricultural managements under different cropping systems lead to spatial heterogeneity of SOC. However, current research often ignores differences in the main controlling factors of SOC under different cropping systems, especially when the cropping pattern is complex, which is not conducive to farmland zoning management. This study aims to (i) obtain the spatial distribution map of six cropping systems by using multi-phase HJ-CCD satellite images; (ii) explore the stratified heterogeneous relationship between SOC and environmental variables under different cropping systems by using the Cubist model; and (iii) predict the spatial map of SOC. The Xiantao, Tianmen, and Qianjiang cities, which are the core agricultural areas of the Jianghan Plain, were selected as the study area. Results showed that the SOC content in rice–wheat rotation was the highest among the six cropping systems. The Cubist model outperformed random forest, ordinary kriging, and multiple linear regression in SOC mapping. The results of the Cubist model showed that cropping system, climate, soil attributes, and vegetation index were important influencing factors of SOC in farmlands. The main controlling factors of SOC under different cropping systems were different. Specifically, summer crop types had a greater influence on spatial variations in SOC than winter crops. Paddy–upland rotation was more affected by river distance and NDVI, while upland–upland rotation was more affected by irrigation-related factors. This work highlights the differentiated main controlling factors of SOC under different cropping systems and provides data support for farmland zoning management. The Cubist model can improve the prediction accuracy of SOC under complex cropping systems.

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Soil as a natural source of greenhouse gas emissions

Alengebawy,

Deka,

Pradhan

et al. 2024

Advances and Technology Development in Greenhouse Gases: Emission, Capture and Conversion

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Comparative analysis of monocropping and mixed cropping systems on selected soil properties, soil organic carbon stocks, and simulated maize yields in drought-hotspot regions of Rwanda

Cited by 4 publications

References 50 publications

Comparing Spatial Sampling Designs for Estimating Effectively Maize Crop Traits in Experimental Plots

Comparing Spatial Sampling Designs for Estimating Effectively Maize Crop Traits in Experimental Plots

Improving soil organic carbon mapping in farmlands using machine learning models and complex cropping system information

Soil as a natural source of greenhouse gas emissions

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